Valence Molecular Connectivity Index Research Articles

Evaluation of the Key Structural Features of Various Butyrylcholinesterase Inhibitors Using Simple Molecular Descriptors.

In this study, we developed several QSAR models based on simple descriptors (such as topological and constitutional) to estimate butyrylcholinesterase (BChE) inhibition potency, pKi (or pIC50), of a set of 297 (289 after exclusion of outliers) structurally different compounds. The models were similar to the best model that we obtained previously for acetylcholinesterase AChE and were based on the valence molecular connectivity indices of second and third order (2χv and 3χv), the number of aliphatic hydroxyl groups (nOH), AlogP Ghose–Crippen octanol–water partition coeff. (logP), and O-060–atom-centred fragments (Al-O-Ar, Ar-O-Ar, R..O..R and R-O-C=X). The best models with two and three descriptors yielded r = 0.787 and S.E. = 0.89, and r = 0.827 and S.E. = 0.81, respectively. We also correlated nine scoring functions, calculated for 20 ligands whose complexes with BChE we found in the Protein Data Bank as crystal structures to pKi (or pIC50). The best correlations yielded PLP1 and PLP2 (Piecewise Linear Pairwise potential functions) with r = 0.619 and 0.689, respectively. Correlation with certain simple topological and constitutional descriptors yielded better results, e.g., 3χv (r = 0.730), on the same set of compounds (N = 20).

Use of connectivity index and simple topological parameters for estimating the inhibition potency of acetylcholinesterase

Acetylcholinesterase (AChE) has proven to be an effective drug target in the treatment of neurodegenerative diseases such as Alzheimer’s, Parkinson’s and dementia. We developed a novel QSAR regression model for estimating potency to inhibit AChE, pKi, on a set of 75 structurally different compounds including oximes, N-hydroxyiminoacetamides, 4-aminoquinolines and flavonoids. Although the model included only three simple descriptors, the valence molecular connectivity index of the zero-order, 0χv, the number of 10-membered rings (nR10) and the number of hydroxyl groups (nOH), it yielded excellent statistics (r = 0.937, S.E. = 0.51). The stability of the model was evaluated when an initial set of 75 compounds was broadened to 165 compounds in total, with the increase of the range of pKi (exp) from 6.0 to 10.2, yielding r = 0.882 and S.E. = 0.89. The predictive power of the model was evaluated by calculating pKi values for 55 randomly chosen compounds (S.E.test = 0.90) from the calibration model created on other 110 compounds (S.E. = 0.89), all taken from the pool of 165 compounds.

RETRACTED: Development of a simple QSAR model for reliable evaluation of acetylcholinesterase inhibitor potency

With the aging of the western population, more and more people are affected by the neurodegenerative Alzheimer's and Parkinson's disease. Inhibitors of acetylcholinesterase (AChE) have proven to be effective in the treatment of disease symptoms. We report the QSAR regression model for the estimation of potency of a set of 94 structurally diverse compounds (oximes, N-hydroxyiminoacetamides, 4-aminoquinolines and flavonoids) to inhibit AChE, pKi (AChE). The model is based on three simple descriptors: the valence molecular connectivity index of the zero-order, 0χv, combined with the number of 10-membered rings (nR10) and number of hydroxyl groups in a molecule (nOH). QSAR model yielded r = 0.947, S.E. = 0.51 and S.E.cv= 0.53; the range of pKi (exp) = 6.03. It showed its stability when the set of 94 compounds was enlarged, comprising 184 compounds in total (r = 0.886, S.E. = 0.85 and S.E.cv = 0.88; the range of pKi (exp) = 10.21), resulting in regression parameters which were similar, although only for 0χv coefficients within the limits of S.E. (0.167(13) and 0.172(16) for the set with 94 and 184 compounds, respectively. The predictive power of the model was shown by the prediction of pKi values for 61 randomly chosen compounds (S.E.test = 0.86) from the calibration model made on the other 123 compounds (S.E. = 0.85), all taken from the pool of 184 compounds. QSAR descriptors 0χv, nR10 and nOH were well chosen for describing the interactions of the AChE active site (amino acid interaction) with ligands through the estimation of the inhibitory potency.

2D- and 3D-QSAR modelling, molecular docking and in vitro evaluation studies on 18β-glycyrrhetinic acid derivatives against triple-negative breast cancer cell line

Triple-negative breast cancers (TNBCs) are one of the most aggressive and complex forms of cancers in women. TNBCs are commonly known for their complex heterogeneity and poor prognosis. The present work aimed to develop a predictive 2D and 3D quantitative structure–activity relationship (QSAR) models against metastatic TNBC cell line. The 2D-QSAR was based on multiple linear regression analysis and validated by Leave-One-Out (LOO) and external test set prediction approach. QSAR model presented regression coefficient values for training set (r2), LOO-based internal regression (q2) and external test set regression (pred_r2) which are 0.84, 0.82 and 0.75, respectively. Five properties, Epsilon4 (electronegativity), ChiV3cluster (valence molecular connectivity index), chi3chain (retention index for three-membered ring), TNN5 (nitrogen atoms separated through 5 bond distance) and nitrogen counts, were identified as important structural features responsible for anticancer activity of MDA-MB-231 inhibitors. Five novel derivatives of glycyrrhetinic acid (GA) named GA-1, GA-2, GA-3, GA-4 and GA-5 were semi-synthesised and screened through the QSAR model. Further, in vitro activities of the derivatives were analysed against human TNBC cell line, MDA-MB-231. The result showed that GA-1 exhibits improved cytotoxic activity to that of parent compound (GA). Further, atomic property field (APF)-based 3D-QSAR and scoring recognise C-30 carboxylic group of GA-1 as major influential factor for its anticancer activity. The significance of C-30 carboxylic group in GA derivatives was also confirmed by molecular docking study against cancer target glyoxalase-I. Finally, the oral bioavailability and toxicity of GA-1 were assessed by computational ADMET studies.Communicated by Ramaswamy H. Sarma

Simple graph-theoretical model for flavonoid binding to P-glycoprotein.

Three sets of flavonoid derivatives (N=32, 40, and 74) and logarithms of their dissociation constants (log Kd) that describe flavonoid affinity toward P-glycoprotein were modelled using six connectivity indices. The best results were obtained with the zero-order valence molecular connectivity index (0χv) for all three sets. Standard errors of the calibration models were around 0.3, and of the constants from the test sets even a little lower, 0.22 and 0.24. Despite using only one descriptor, our model proved better in internal (cross-validation) and especially in external (test set) statistics than much more demanding methods used in previous 3D QSAR modelling.

Stability prediction of copper(II) complexes with peptides containing cysteinic disulfide bridge by models based on the connectivity index 3χv

The article is a continuation of our work on the modeling of the stability of complex compounds. For copper(II) complexes with five protonated and deprotonated cyclic oligopeptides containing a disulfide bridge, we developed two common models. The variables used in both models are valence molecular connectivity index of the third order, 3χv, and indicator variable(s), In or In1 and In2. The model with In1 and In2 (they represent the number of terminal and non-terminal glycine residues, respectively) yielded better estimates (S.E.cv = 0.51) than the model with In (= In1 + In2); S.E.cv = 0.60.

Predictive modeling of chemical toxicity towards Pseudokirchneriella subcapitata using regression and classification based approaches

Biodiversity nurturing may be a valuable pathway in controlling chemical stress on the ecosystem. In the present work, in silico studies have been performed to develop regression based quantitative structure toxicity relationship (QSTR) models using a data set containing 105 organic chemicals for the prediction of 48-h chemical toxicity towards Pseudokirchneriella subcapitata. Classification based linear discriminant analysis (LDA) was also performed to distinguish chemicals into toxic and nontoxic groups using the same data set. The developed models were found to possess good predictive quality in terms of internal, external and overall validation parameters. The regression based QSTR model suggests that second order molecular connectivity index (molecular size and lipophilicity), density (aromaticity), relative shape of molecules (cyclicity/aromaticity), and specific molecular fragments of the chemicals are important properties of chemicals to exert their toxicity on P. subcapitata. The classification based LDA QSTR model suggested that fused ring aromatic systems, secondary carbon atom fragments, second order valence molecular connectivity indices (molecular size and branching) and molecular weight are the distinguishing features to differentiate chemicals into toxic and nontoxic groups.

Stability prediction of Cu2+, Ni2+ and Zn2+ N-salicylidene-aminoacidato complexes by models based on connectivity index 3 χ v

AbstractThis paper presents models for the estimation of stability constants (K 1 and β 2) of nickel(II), copper(II) and zinc(II) mono- and bis-complexes with 5 Schiff bases (salicylideneglycine, salicylidenealanine, salicylideneserine, salicylidenephenylalanine, and salicylidenetyrosine). The models were based on the molecular-graph theory and valence molecular connectivity index of the 3rd order, 3χv , derived from it. Univariate linear models were developed for each metal separately, while in the common models for two and three metals, the indicator variable, In, was introduced. The standard error of models for the log K 1 constant was less than 0.12, while for log β 2 models, the S.E. did not exceed 0.14.

The Hydrogen Perturbation in Molecular Connectivity Indices and their Application to a QSPR Study

Although chemical graphs do not show the difference between various atoms and electron lone pairs, the use of pseudo-graphs is a remedy. Modified molecular connectivity indices (mMCIs) have been suggested as showing the role of hydrogen atoms that are also useful in distinguishing isomers. A new algorithm for the δv number, the basic parameter of molecular connectivity indices (MCIs), has recently been proposed. This algorithm, which is centered on graph concepts such as complete graphs and general graphs, encodes the information of the bonded hydrogen atom on different atoms through a perturbation parameter that requires no new graph concepts. In this study, hydrogen perturbations in valence molecular connectivity indices were applied as structural descriptors for organic compounds in quantitative structure property relationship studies on the molar volume and molar refraction of liquid alkanes, alkenes and alcohols. The results show that, in most cases, these indices give improved correlations compared with the original MCIs.

Investigating the Quantitative Structure-Activity Relationships for Antibody Recognition of Two Immunoassays for Polycyclic Aromatic Hydrocarbons by Multiple Regression Methods

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous contaminants found in the environment. Immunoassays represent useful analytical methods to complement traditional analytical procedures for PAHs. Cross-reactivity (CR) is a very useful character to evaluate the extent of cross-reaction of a cross-reactant in immunoreactions and immunoassays. The quantitative relationships between the molecular properties and the CR of PAHs were established by stepwise multiple linear regression, principal component regression and partial least square regression, using the data of two commercial enzyme-linked immunosorbent assay (ELISA) kits. The objective is to find the most important molecular properties that affect the CR, and predict the CR by multiple regression methods. The results show that the physicochemical, electronic and topological properties of the PAH molecules have an integrated effect on the CR properties for the two ELISAs, among which molar solubility (Sm) and valence molecular connectivity index (3χv) are the most important factors. The obtained regression equations for RisC kit are all statistically significant (p < 0.005) and show satisfactory ability for predicting CR values, while equations for RaPID kit are all not significant (p > 0.05) and not suitable for predicting. It is probably because that the RisC immunoassay employs a monoclonal antibody, while the RaPID kit is based on polyclonal antibody. Considering the important effect of solubility on the CR values, cross-reaction potential (CRP) is calculated and used as a complement of CR for evaluation of cross-reactions in immunoassays. Only the compounds with both high CR and high CRP can cause intense cross-reactions in immunoassays.

A Quantitative Structure-Activity Relationship Study on a Few Series of Anti-hepatitis C Virus Agents

A 2-Dimensional Quantitative Structure-Activity Relationship study has been performed on 2 series of hepatitis C virus (HCV) inhibitors, i.e., Isothiazoles and Thiazolones. In each case significant correlations are found between the anti-HCV potencies and some physicochemical, electronic and steric properties of the compounds, indicating that for the first series the activity is controlled by density and two indicator parameters (one for halogen and other for methyl), while for the second series density, Hammett constant and Kier's first order valence molecular connectivity index are important for anti-HCV activity. The validity of the correlation has been judged by leave-one-out jackknife procedure and predicting the activity of some test compounds. Using the correlations obtained, some new compounds of high potency have been predicted in each series.

Toxicity Study for Halogenated Aromatics to Mixed Bacteria in River Water

The bacterial growth inhibition test was used to determine toxicity of the halogenated aromatics on the mixed bacteria from the Yangtze River. The concentration values causing 50% reduction of bacteria growth for 24 hr (IC50) and the maximum tolerance concentration for 7 d (MATC) were gained according to the concentration-response curves. An obvious linear relationship was found between the two toxicity descriptors. The following quantitative structure- activity relationships were developed by using the valence molecular connectivity index method: -lgIC50=0.8266Xpcv- 0.2363Xpv+3.811 (R2=0.833) and -lgMATC=1.2016Xpcv-0.3783Xpv+4.614 (R2=0.900). The two equations were found to fit well. It suggested that the models derived from the molecular connectivity index could be used successfully to predict the toxicity of halogenated aromatic compounds.

Graph-Theoretical Indices based on Simple, General and Complete Graphs

Valence molecular connectivity indices are based on the concept of valence delta, d v, that can be derived from general chemical graphs or chemical pseudographs. A general graph or pseudograph has multiple edges and loops and can be used to encode, through the valence delta, chemical entities. Two graph-theoretical concepts derived from chemical pseudographs are the intrinsic (I) and the electrotopological state (E) values, which are the used to define the valence delta of the pseudoconnectivity indices, ?I,S. Complete graphs encode, through a new valence delta, the core electrons of any atoms in a molecule. The connectivity indices, either valence connectivity or pseudoconnectivity, are the starting point to develop the dual connectivity indices. The dual indices show that not only can they assume negative values but also cover a wide range of numerical values. The central parameter of the molecular connectivity theory, the valence delta, defines a completely new set of connectivity indices, which can be distinguished by their configuration and advantageously used to model different properties and activities of compounds.

A QSAR Study on Some Series of Anti-Hepatitis C Virus (HCV) Agents

Hepatitis C virus (HCV) is a major human pathogen, infecting an estimated 170 million persons worldwide, roughly five times the number infected by HIV type 1. A substantial fraction of these HCV infected individuals develop serious progressive liver disease, including cirrhosis and hepatocellular carcinoma. The current treatment regimen suffers from various side effects. These limitations encourage the search of more effective HCV inhibitors. To achieve this objective a quantitative structure-activity relationship (QSAR) study has been made on three different series of anti-HCV agents that are comprised of pyrrolo[2,3-d]pyrimidine nucleoside derivatives, benzimidazole-coumarin conjugates, and ribonucleoside analogues, respectively. The QSAR analysis reveals that anti-HCV potencies of these series of compounds are controlled by a topological parameter (Kiers first order valence molecular connectivity index 1χv), lipophilicity, and the molar refractivity of the compounds, respectively. Based on these results, the mechanisms of drug-receptor interactions of these compounds are discussed, which may be of great help to design and develop more anti-HCV agents.

Toxicity assessment of organic pollutants: Reliability of bioluminescence inhibition assay and univariate QSAR models using freshly prepared Vibrio fischeri

The toxicity of 14 industrially relevant organic chemicals was determined using freshly grown Vibrio fischeri bioluminescence inhibition assay. The results were compared to lyophilized V. fischeri, 96h fish, 48h Daphnia magna and 95h green algae bioassays. Reliability of octanol-water partition coefficient (K(ow)), and first order simple and valence molecular connectivity index ((1)chi, (1)chi(v)) based regression models for predicting toxicity to V. fischeri was studied. Correlations were obtained between freshly grown V. fischeri data (Log(EC50)) and Log(K(ow)), molecular connectivity indices ((1)chi, (1)chi(v)), energy of the highest occupied (E(HOMO)) and lowest unoccupied (E(LUMO)) molecular orbitals, and their difference (E(LUMO)-E(HOMO)). A good match was observed between V. fischeri assay conducted with freshly grown and lyophilized culture (r2=0.90). Good correlations (r2>0.95) were obtained with all the other bioassays after excluding compounds with Log(K(ow)) less than 2.0. Available regression models based on Log(K(ow)) and (1)chi(v) yielded lower toxicity values. V. fischeri bioassay showed fairly good correlation with Log(K(ow)), (1)chi and (1)chi(v) (r2>0.75) but poor correlation with E(HOMO), E(LUMO) and (E(LUMO)-E(HOMO)) in presence of polar compounds. E(HOMO) and E(LUMO) values are affected by polarity and can be used along with Log(K(ow)) and (1)chi(v) for generating better predictive models.

A Quantitative Structure-Activity Relationship Study on a Novel Series of Hydroxamic Acid Analogs Acting as Matrix Metalloproteinase Inhibitors

A Quantitative structure-activity relationship study (QSAR) has been made on a novel series of hydroxamic acid-based matrix metalloproteinase (MMP) inhibitors. The inhibition potencies of the compounds against two MMPs, MMP-2 and MMP-3, are found to be significantly correlated with Kiers first-order valence molecular connectivity index (1xv) and Hammetts electronic constant of the substituents. It is thus found that in addition to zinc-binding group of inhibitors interacting with the zinc ion present in the MMPs, some other groups also in the molecules may have some sorts of physicochemical interactions with the sub-sites of MMPs Keywords: Quantitative structure-activity relationship, Matrix metalloproteinase inhibitors, Hydroxamic acids

Quantitative structure activity relationship (QSAR) for toxicity of chlorophenols on L929 cells in vitro

Quantitative structure activity relationship (QSAR) were developed to predict toxicity of chlorophenols by correlating LC 50 values with five molecular descriptors, chosen to represent lipophilic, electronic and steric effects: the n-octanol/water partition coefficient (log K ow), the constant of Hammett (∑ σ), the acid dissociation constant (p K a), the order valence molecular connectivity index ( 1 χ v) and the perimeter of the efficacious section (∑ D g). The results of the regression analysis showed that log K ow and ∑ D g are the dominant (canonical) predictive factors in determining toxicity of chlorophenols to the cells during 24 h exposures, while log K ow was the only dominant predictive factor contributing to toxicity during in 48 h exposures. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to investigate predictive relationships of the cytotoxicity of chlorophenols and develop visual 3D-QSAR models. The CoMFA model, in which the contribution of the electrostatic field to the biological activity was greater than that of the steric field, exhibited both high consistency and predictability ( r 2 = 0.968, Q 2 = 0.891 for 24 h exposure; but the relationship was poorer for the 48 h exposure: r 2 = 0.727, Q 2 = 0.394). The CoMSIA model used in this study contained three fields: electrostatic, hydrophobic and steric, of which the relative contribution to the biological activity was 0.767:0.225:0.008. In addition, according to the models for 24 h and 48 h. The time-dependent toxicity and potential mechanisms for inhibition of L929 cells was discussed.

QSAR analysis of some 5-amino-2-mercapto-1,3,4-thiadiazole based inhibitors of matrix metalloproteinases and bacterial collagenase

A quantitative structure–activity relationship (QSAR) study has been performed on 5-amino-2-mercapto-1,3,4-thiadiazole based inhibitors of matrix metalloproteinases (MMPs) and a bacterial collagenase known as Clostridium histolyticum collagenase (ChC) to understand the structural features influencing the affinity of these inhibitors towards the enzyme. The compounds in the selected series were characterized by topological and fragmental descriptors calculated using QuaSAR module of molecular operating environment (MOE). An indicator variable was also assigned to account for the presence of amide function in vicinity of sulfonamide group in the parent structure. Correlations between different inhibitory activities and calculated predictor variables were established through stepwise multiple regression employing the method of least squares. The results of the study indicates that MMP inhibitory activity of 5-amino-2-mercapto-1,3,4-thiadiazoles can be successfully explained in terms of topology of the molecule. The obtained correlations also suggest that increase in the number of fluorine atoms in the aromatic ring will augment inhibitory activity of these molecules against all the MMPs probably by virtue of hydrogen bond interaction with some complementary groups in the active site of the enzymes. One prime requirement for better inhibition of MMPs (except for MMP-1) and ChC identified from the present study is the presence of amide function in vicinity of sulfonamide group in the parent structure as suggested by the presence of indicator variable in almost all correlations. While MMP-1 and ChC inhibitory activity of the compounds studied is shown to be dependent on Kier’s first order carbon valence molecular connectivity index indicating that increase in branching and presence of heteroatoms in the molecule will improve the MMP-1 and ChC inhibitory potency of 5-amino-2-mercapto-1,3,4-thiadiazoles, correlations derived for other enzymes (MMP-2, MMP-8, MMP-9) are quite similar. In addition to the number of fluorine atoms and presence of indicator variable, MMP-2, MMP-8 and MMP-9 inhibitory activity of 5-amino-2-mercapto-1,3,4-thiadiazoles is found to be dependent on Kier’s alpha modified index of third order in such a way that infer, terminally branched functions will increase the affinity of these molecules to the MMPs.

Quantitative structure and retention relationships for gas chromatographic data: Application to alkyl pyridines on apolar and polar phases

Quantitative structure and retention relationships (QSRR) have been developed to model gas chromatographic retention data of alkyl pyridines on apolar (branched alkane) and polar (primary alcohol) stationary phases. The retention properties analyzed are Kovats retention index, I; partial molar enthalpy, Δ H; partial molar entropy, Δ S and partition coefficient, log K. Using the seven valence molecular connectivity indices ( χ) calculated for the 18 alkyl pyridines, regression models are generated to predict the retention properties. The best model (model A) obtained with the descriptors χ P V 1 , χ P V 3 and χ CH V 6 was unable to produce a satisfactory statistical performance and correct order of elution. The model has been modified (model B) by including steric parameter s, which has been empirically derived by considering the steric effects due to the presence of alkyl groups at the ortho and meta positions. The modified model predicts the correct order of elution for all the alkyl pyridines and good correlation coefficients, r. The r values obtained for I, Δ H, Δ S and log K are: r = 0.955, 0.975, 0.984 and 0.955 (model A) become r = 0.999, 0.996, 0.990 and 0.999 (model B) on the apolar stationary phase and r = 0.931, 0.926, 0.911 and 0.931 (model A) become r = 0.999, 0.992, 0.958 and 0.999 (model B) on the polar stationary phase.

Design, synthesis, antibacterial, and QSAR studies of myristic acid derivatives

A series of esters and amides of myristic acid was synthesized and tested in vitro for antibacterial activity against Gram-positive and Gram-negative bacteria. All the compounds showed activity comparable to that of the standard drug, ciprofloxacin. The structural characteristics governing antibacterial activity of myristic acid derivatives was studied using QSAR methodology. The results showed that the antibacterial activity could be modeled using the topological descriptor, valence molecular connectivity index. The predictive ability of the models was cross-validated by construction of a test set. The low residual activity and high cross-validated r 2 values ( r cv 2 ) observed indicated the predictive ability of the developed QSAR models.